004 - Spatial Analyses to Identify and Characterize NYC Neighborhoods with Low Access to Pharmacy

Neighborhood-level social determinants of health, such as access to pharmacy, may exacerbate health disparities. However, differential pharmacy access in an urban environment, such as New York City (NYC), and its impact on medication adherence among residents, is not clearly understood. In this work, we aim to characterize variations in pharmacy access in NYC neighborhoods and assess social and neighborhood characteristics of low pharmacy access.

Data on number of pharmacies per 10,000 residents (pharmacy densities) within a census tract were collected from the National Neighborhood Data Archive. Local indicators of spatial association (LISA), such as Local Moran Index and Geti Ord Statistic, were used to identify clusters of census tracts with systematically higher (hot-spot) or lower (cold-spot) pharmacy densities than expected under random spatial distribution. The hot-spot and cold-spot census tracts were then summarized using census tract level socio-economic status (SES) index (between 0-100), vehicle access and other demographic variables, derived from 5-year estimates of the U.S. Census Bureau's 2019 American Community Survey as well as walkability from the City Health Dashboard.

Of the 1,941 census tracts in NYC, 21 were identified to have lower than expected pharmacy densities, whereas 18 had higher than expected pharmacy densities. Neighborhoods with low pharmacies were all either in Brooklyn (9/21, 42.9%) or Queens (12/21, 57.2%), and had a median value of 0 (IQR= 0, 0.98), compared to the overall NYC median of 2.5 (IQR=0, 5.5) pharmacies per 10,000 residents. Neighborhoods with the lowest pharmacy density had the highest proportion of non-Hispanic Black residents (44.6%; SD=34.9%) and lowest SES (mean = 44.6, SD = 11.9), compared to 4.8% (SD = 9.3%) non-Hispanic Black residents and mean SES of (67.2, SD = 19.9) in the high pharmacy density neighborhoods. The association between differential access to pharmacies and medication non-adherence among patients with heart failure will also be presented. As a next analytical step, we will implement Bayesian spatial conditional autoregressive models to model pharmacy densities adjusting for multiple neighborhood characteristics.

Our findings highlight inequities in pharmacy access and support investigating place-based approaches to inform policies that address neighborhood-level health disparities among NYC residents.

Social Determinants of Health

Pharmacy Access

Medication adherence

Bayesian Modeling

Disparities

Built Environments 

Mid-Level

Knowledge

International Conference on Health Policy Statistics 2023